class_docstr: Side-Aware Boundary Localization (SABL) for RoI-Head.
  
  Side-Aware features are extracted by conv layers
  with an attention mechanism.
  Boundary Localization with Bucketing and Bucketing Guided Rescoring
  are implemented in BucketingBBoxCoder.
  
  Please refer to https://arxiv.org/abs/1912.04260 for more details.
  
  Args:
      cls_in_channels (int): Input channels of cls RoI feature.             Defaults to 256.
      reg_in_channels (int): Input channels of reg RoI feature.             Defaults to 256.
      roi_feat_size (int): Size of RoI features. Defaults to 7.
      reg_feat_up_ratio (int): Upsample ratio of reg features.             Defaults to 2.
      reg_pre_kernel (int): Kernel of 2D conv layers before             attention pooling. Defaults to 3.
      reg_post_kernel (int): Kernel of 1D conv layers after             attention pooling. Defaults to 3.
      reg_pre_num (int): Number of pre convs. Defaults to 2.
      reg_post_num (int): Number of post convs. Defaults to 1.
      num_classes (int): Number of classes in dataset. Defaults to 80.
      cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
      reg_offset_out_channels (int): Hidden and output channel             of reg offset branch. Defaults to 256.
      reg_cls_out_channels (int): Hidden and output channel             of reg cls branch. Defaults to 256.
      num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
      num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
      reg_class_agnostic (bool): Class agnostic regresion or not.             Defaults to True.
      norm_cfg (dict): Config of norm layers. Defaults to None.
      bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
      loss_cls (dict): Config of classification loss.
      loss_bbox_cls (dict): Config of classification loss for bbox branch.
      loss_bbox_reg (dict): Config of regression loss for bbox branch.
docstr: Split batch bbox prediction back to each image.
docstr_tok: ["Split","batch","bbox","prediction","back","to","each","image","."]
code: def bbox_pred_split(self, bbox_pred, num_proposals_per_img):
      """Split batch bbox prediction back to each image."""
      bucket_cls_preds, bucket_offset_preds = bbox_pred
      bucket_cls_preds = bucket_cls_preds.split(
          num_proposals_per_img, 0
      )
      bucket_offset_preds = bucket_offset_preds.split(
          num_proposals_per_img, 0
      )
      bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds))
      return bbox_pred
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
  from mmcv.runner import force_fp32
  from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
  from mmdet.models.builder import HEADS, build_loss
  from mmdet.models.losses import accuracy
label_desc: Use image node_id 9 for calling the SABLHead obj's underlying member method code with example usage: obj.bbox_pred_split(bbox_pred, num_proposals_per_img) and returns: bbox_pred